Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Morphological tagging: data vs. dictionaries
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Inducing multilingual text analysis tools via robust projection across aligned corpora
HLT '01 Proceedings of the first international conference on Human language technology research
Inducing multilingual POS taggers and NP bracketers via robust projection across aligned corpora
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Minimally supervised morphological analysis by multimodal alignment
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Bootstrapping parsers via syntactic projection across parallel texts
Natural Language Engineering
Tagging Portuguese with a Spanish tagger using cognates
CrossLangInduction '06 Proceedings of the International Workshop on Cross-Language Knowledge Induction
Transferring structural markup across translations using multilingual alignment and projection
Proceedings of the 10th annual joint conference on Digital libraries
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Annotated corpora are valuable resources for NLP which are often costly to create. We introduce a method for transferring annotation from a morphologically annotated corpus of a source language to a target language. Our approach assumes only that an unannotated text corpus exists for the target language and a simple textbook which describes the basic morphological properties of that language is available. Our paper describes experiments with Polish, Czech, and Russian. However, the method is not tied in any way to these languages. In all the experiments we use the TnT tagger ([3]), a second-order Markov model. Our approach assumes that the information acquired about one language can be used for processing a related language. We have found out that even breathtakingly naive things (such as approximating the Russian transitions by Czech and/or Polish and approximating the Russian emissions by (manually/automatically derived) Czech cognates) can lead to a significant improvement of the tagger’s performance.